Two-stage dynamic signal detection: A theory of choice, decision time, and confidence.

2010/01/01 by Timothy J. Pleskac, Jerome R. Busemeyer · 786 citations
Decision Sciences · Mathematics · Neuroscience · #Artificial intelligence #Categorization #Computer science #Confidence distribution #Confidence interval #Decision-Making and Behavioral Economics #Detection theory #Econometrics #Forecasting Techniques and Applications #Interrupt #Mathematics #Neural and Behavioral Psychology Studies #Process (computing) #Statistics

paper · doi:10.1037/a0019737

published in Psychological Review 117(3), 864-901 (American Psychological Association)

openalex publication_date 2010/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

The 3 most often-used performance measures in the cognitive and decision sciences are choice, response or decision time, and confidence. We develop a random walk/diffusion theory-2-stage dynamic signal detection (2DSD) theory-that accounts for all 3 measures using a common underlying process. The model uses a drift diffusion process to account for choice and decision time. To estimate confidence, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The model explains all known interrelationships between the 3 indices of performance. Furthermore, the model also accounts for the distributions of each variable in both a perceptual and general knowledge task. The dynamic nature of the model also reveals the moderating effects of time pressure on the accuracy of choice and confidence. Finally, the model specifies the optimal solution for giving the fastest choice and confidence rating for a given level of choice and confidence accuracy. Judges are found to act in a manner consistent with the optimal solution when making confidence judgments.

Citations

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